Introduction To Data Science By Lavanya Vijayan

Introduction To Data Science By Lavanya Vijayan

Data Science is shaping the business world like never before, and demand for data scientists has never been higher. In this course, data scientist and Python trainer Lavanya Vijayan shares the fundamentals of Data Science and what sets it apart from other information-driven techniques. Lavanya then breaks down the components of Data Science, covering workflow and toolsets such as programming languages and specialized resources like Jupyter Notebooks. Lavanya centers on techniques like exploratory data analysis, data cleaning, and data visualization. She also explores the topics of sampling, testing, and classification. Through this course you'll be prepared to execute basic data analysis and reporting, opening up the opportunities to further your career in this increasingly relevant field.
Data Science
Explainer
Personal Development
Content Rating: U

Episodes

01:00 min
In this course, data scientist and Python trainer Lavanya Vijayan shares the fundamentals of Data Science and what sets it apart from other information-driven techniques.
03:35 min
Data Science is rapidly increasing in popularity and demand and is a valuable skill whether you're pursuing it as a career or using the principles in your existing job. After watching this video you'll know what data science is.
01:42 min
Data Science can be used across numerous fields and offers important benefits to the world around us. After watching this video you'll be able to explain the value of data science.
00:49 min
Data scientists follow a specific workflow. After watching this video you'll be able to describe the data science life cycle is and the main goal of each stage.
03:44 min
Data design, the process of data collection, is important in data science. After watching this video you'll know how sampling reduces bias in data design.
02:10 min
Two of the most popular computing languages for data science are currently Python and R. After watching this video you'll know the key differences between them.
03:43 min
Before you dive in, you will need to set up your environment. If you are not familiar with Jupyter, this is a great opportunity to familiarize yourself with this powerful platform. After watching this video you'll be able to setup Jupyter on your computer.
02:40 min
Datasets can be structured in many ways. Datasets that have tabular structure are easier to work with. After watching this video you'll be able to interact with tabular data.
10:44 min
Once you have access to a dataset, you will need to interact with it to find out how you can use this data to draw conclusions or solve a problem. After watching this video you'll be able to read in tabular data with Python.
06:06 min
After watching this video you'll be able to start gathering insights from your dataset. Tabular data manipulation and drawing conclusions from data is a crucial component of data science.
02:51 min
The goal of data science is to identify and answer specific questions. After watching this video you'll be able to evaluate what questions you want to answer and what types of questions are ideal for your scenario.
00:47 min
Conducting exploratory data analysis (EDA) is the next crucial stage in the data science life cycle. After watching this video you'll be able to explain the definition of EDA.
03:08 min
Statistical data types are at the core of most data science operations. These include numerical and categorical data. After watching this video you'll know these different data types.
06:16 min
EDA involves determining the key properties of the data you have. After watching this video you'll be able to use the different properties of data in your next analysis.
01:21 min
Data cleaning is a crucial stage in the data science life cycle. After watching this video you'll know what data cleaning is and how it is used.
06:24 min
It is important to understand how the data was generated before cleaning it. After watching this video, you will know the main questions to ask before cleaning.
01:00 min
After determining the granularity, scope, temporality, and faithfulness of your data, it is important to understand the relationships among your data. After watching this video you'll know what data visualization is and how it's used in data science.
05:38 min
After watching this video you'll be ready to visualize qualitative data and describe the difference between qualitative and quantitative. Different types of visualization correspond to different types of data.
08:09 min
Different types of visualization correspond to different types of data. After watching this video you'll be prepared to visualize quantitative data.
00:45 min
Inference is also an integral part of the last stage in the data science life cycle. After watching this video you'll be able to describe what inference is and how it's used.
06:33 min
As you expand your data science skill set, you'll be ready to tackle hypothesis testing. After watching this video you'll know what a hypothesis test is, how it is used, and how to set one up.
13:20 min
As you expand your data science skill set, you'll be ready to tackle permutation testing. After watching this video you'll know what a permutation test is, how it is used, and how to set one up.
09:45 min
Oftentimes in data science we look to answer questions using the data that we have available. After watching this video you'll be able to solve complex questions by bootstrapping your confidence interval.
02:18 min
Classification is an important machine learning technique. After watching this video you'll be able to describe classification and explain how it is used.
02:50 min
k-NN or k-Nearest Neighbor is a common Data Science algorithm. After watching this video you'll know what the k-Nearest Neighbor algorithm is and how to use it to classify data.
01:51 min
Find out how to practice and hone your data science skills in this video.